code: def test_cumprod_forward_backward_large(
      seed,
      shape,
      axis,
      exclusive,
      reverse,
      with_mask,
      with_random_zero_pos,
      zero_pos,
      ctx,
      func_name,
  ):
      """Test for large input cases.
      Instead of numerical gradient calculation, `ref_grad_cumuprod` is used here because calculation of numerical gradient takes long time with large input.
      """
      from nbla_test_utils import function_tester
  
      rng = np.random.RandomState(seed)
      input = create_cumprod_input(
          rng, shape, axis, with_mask, with_random_zero_pos, zero_pos
      )
  
      # Half test is disabled since CumProd for fp16 is not implemented currently.
      function_tester(
          rng,
          F.cumprod,
          ref_cumprod,
          [input],
          ref_grad=ref_grad_cumprod,
          func_args=[axis, exclusive, reverse],
          ctx=ctx,
          func_name=func_name,
          disable_half_test=True,
      )
  
file_path: nnabla/python/test/function/test_cumprod.py
imports: import pytest
  import numpy
  import nnabla
  import nnabla.functions
  from nbla_test_utils import list_context
  from refs import cumprod
  from refs import cumprod_backward
label_desc: Use image node_id 3 for calling a global function with example usage: test_cumprod_forward_backward_large(seed, shape, axis, exclusive, reverse, with_mask, with_random_zero_pos, zero_pos, ctx, func_name) without return types